title: Automated determination of metastases in unstructured radiology reports for eligibility screening in oncology clinical trials.
abstract: Enrolling adequate numbers of patients that meet protocol eligibility criteria in a timely manner is critical, yet clinical trial accrual continues to be problematic. One approach to meet these accrual challenges is to utilize technology to automatically screen patients for clinical trial eligibility. This manuscript reports on the evaluation of different automated approaches to determine the metastatic status from unstructured radiology reports using the Clinical Trials Eligibility Database Integrated System (CTED). The study sample included all patients (N = 5,523) with radiologic diagnostic studies (N = 10,492) completed in a two-week period. Eight search algorithms (queries) within CTED were developed and applied to radiology reports. The performance of each algorithm was compared to a reference standard which consisted of a physician's review of the radiology reports. Sensitivity, specificity, positive, and negative predicted values were calculated for each algorithm. The number of patients identified by each algorithm varied from 187 to 330 and the number of true positive cases confirmed by physician review ranged from 171 to 199 across the algorithms. The best performing algorithm had sensitivity 94%, specificity 100%, positive predictive value 90%, negative predictive value 100%, and accuracy of 99%. Our evaluation process identified the optimal method for rapid identification of patients with metastatic disease through automated screening of unstructured radiology reports. The methods developed using the CTED system could be readily implemented at other institutions to enhance the efficiency of research staff in the clinical trials eligibility screening process.
authors.affiliation: Massey Cancer Center, Virginia Commonwealth University, 23298, USA.
authors.forename: Valentina I
authors.initials: VI
authors.lastname: Petkov
authors.forename: Lynne T
authors.initials: LT
authors.lastname: Penberthy
authors.forename: Bassam A
authors.initials: BA
authors.lastname: Dahman
authors.forename: Andrew
authors.initials: A
authors.lastname: Poklepovic
authors.forename: Chris W
authors.initials: CW
authors.lastname: Gillam
authors.forename: James H
authors.initials: JH
authors.lastname: McDermott
mesh_terms: D000465:Algorithms; D001331:Automation; D002986:Clinical Trials as Topic / Q000379:methods*; D016208:Databases, Factual; D006801:Humans; D008495:Medical Oncology / Q000379:methods; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity
keywords: Clinical trials; automation; eligibility screening; information extraction; metastases; radiology reports